Developing a Decision Tree Algorithm for Detecting Agroforestry and Monoculture Coffee Plantations Using Landsat 8 Imagery: A Case Study inBandung Regency, Indonesia
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brute force, coffee agroforestry, coffee monoculture, decision tree, landsat 8, machine learning
Abstract
Data on the potential of coffee commodities in Bandung Regency is still mixed with data on other commodities. Therefore, the study aims to develop an algorithm that provides accurate spatial information through maps for both coffee plantations in agroforestry and monoculture systems. This study integrates the data derived from remotely sensed data and data derived using socio-geobiophysical aspects, such as elevation, slope, distance from the road and rivers, proximity of the settlements, population density, proximity of villages, and a visually-based land-use-land cover map. The importance value for each variable was computed using several criteria, such as information gain, Gini index, and gain ratio. Meanwhile, the brute force method was applied to select the most significant variables in the model. The study found that the most significant variables for identifying coffee agroforestry and monoculture were ARVI, EVI, GARI, NRGI, and VDVI, as well as DEM, slope, proximity to roads, and visual-based LULC, using the criterion of information gain. The use of existing land-use and cover maps was the most influential variable in the model. The algorithm achieved an overall accuracy (OA) of 84.65% and a kappa accuracy (KA) of 82.60%. Based on overall accuracy and high kappa accuracy, the maps produced facilitate local governments and cooperatives in planning specific interventions for coffee-producing areas, supporting policies related to sustainable agriculture, climate-smart agroforestry expansion, and supply chain traceability.
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